• 한국소음진동공학회논문집
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• 제16권1호
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• pp.40-49
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• 2006

A novel disturbance measurement method, model based disturbance observer (MBDO) for optical disk drives (ODDs), is proposed and the disturbance analysis using the proposed method is performed under various conditions. In ODDs, the quantitative and qualitative analysis for the generated disturbance during normal operation is very important to successful servo loop design. However, the disturbance measurement is difficult, and high precision measurement is necessary. Furthermore, the conventional disturbance measurement method using a LDV (laser Doppler vibrometer) has many difficulties in eccentricity direction due to the vertical movement of an optical disk. To solve this problem, the MBDO is proposed. First, the relationship between the servo loop for ODDs and the generated disturbance are briefly reviewed. Second, the principle of the MBDO is introduced, and the disturbance measurement results, which are measured by the MBDO and a LDV, are compared. In these experiments, test DVD-ROM disks are used to generate quantitative/qualitative disturbance. Then, the disturbance analysis under various conditions is performed using waterfall technique. This technique clearly shows the disturbance trend from the inner part of an optical disk to the outer part of it. Finally, the various disturbances measurement results are summarized and some remarks for it are commented.

• 전력전자학회:학술대회논문집
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• 전력전자학회 2003년도 춘계전력전자학술대회 논문집(1)
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• pp.393-397
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• 2003

This paper presents neural load torque observer tha used to deadbeat load torque observer and regulation of the compensation gun by parameter estimator. As a result, the response of PMSM follows that of the nominal plant. The load torque compensation method is compose of a neural deadbeat observer. To reduce of the noise effect, the post-filter, which is implemented by MA process, is adopted. The parameter compensator with RLSM (recursive least square method) parameter estimator is adopted to increase the performance of the load torque observer and main controller. The parameter estimator li combined with a high performance neural torque observer to resolve the problems. As a result, the proposed control system becomes a robust and precise system against the load torque and the parameter variation. A stability and usefulness, through the verified computer simulation, are shown in this paper

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년 학술대회 논문집 정보 및 제어부문
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• pp.339-341
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• 2006

This paper presents a new method on controller design of brushless dc motors. In such drives the current ripples are generated by motor inductance in stator windings and the back EMF. To suppress the current ripples the current controller is generally used. To minimize the size and the cost of the drives it is desirable to control motors without the current controller and the current sensing circuits. To estimate the motor current it is modeled by a neural network that is configured as an output-error dynamic system. The identified model is essentially a one step ahead prediction structure in which fast inputs and outputs are used to calculate the current output. Using the model, effective estimator to compensate the effects of disturbance has been designed. The effectiveness of the proposed current estimator is verified through experiments.

• 전력전자학회:학술대회논문집
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• 전력전자학회 2002년도 전력전자학술대회 논문집
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• pp.285-288
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• 2002

This paper presents external load disturbance compensation that used to deadbeat load torque observer and regulation of the compensation gain by parameter estimator As a result, the response of PMSM follows that of the nominal plant. The load torque compensation method is compose of a deadbeat observer To reduce of the noise effect, the post-filter, which is implemented by MA process, is adopted. The parameter compensator with RLSM(recursive least square method) parameter estimator is suggested to increase the performance of the load torque observer and main controller The proposed estimator is combined with a high performance load torque observer to resolve the problems. As a result, the proposed control system becomes a robust and precise system against the load torque and the parameter variation. A stability and usefulness, through the verified computer simulation, are shown in this paper.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2000년도 하계학술대회 논문집 D
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• pp.2374-2376
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• 2000

In this paper, we propose an indirect vector control method using Adaptive Neuro-Fuzzy Inference System (ANFIS) parameter estimator. It estimates the rotor time constant when the indirect vector control of induction motor is applied. We use the stator current error that is difference between the current command and estimated current calculated from terminal voltage and current. And two induced current estimate equations are used in training ANFIS.The estimator is trained by the hybrid learning algorithm. Simulation results shows good performance under load disturbance and motor parameter variations.

• 전력전자학회:학술대회논문집
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• 전력전자학회 2002년도 추계학술대회 논문집
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• pp.49-52
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• 2002

This paper presents neural load torque observer that used to deadbeat load torque observer and regulation of the compensation gain by parameter estimator. As a result, the response of PMSM follows that of the nominal plant. The load torque compensation method is compose of a neural deadbeat observer. To reduce of the noise effect, the post-filter, which is implemented by MA process, is adopted. The parameter compensator with RLSM (recursive least square method) parameter estimator is adopted to increase the performance of the load torque observer and main controller. The parameter estimator is combined with a high performance neural torque observer to resolve the problems. As a result, the proposed control system becomes a robust and precise system against the load torque and the parameter variation. A stability and usefulness, through the verified computer simulation, are shown in this paper.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2013년도 추계학술대회 논문집
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• pp.713-714
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• 2013

• Journal of Advanced Marine Engineering and Technology
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• 제33권8호
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• pp.1196-1202
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• 2009

본 논문에서는 ROV 제어 시스템 구축을 위하여 관측기를 이용한 수중환경 추정기 설계를 다룬다. 먼저, 수중환경변화는 ROV 시스템의 입력항목에 가해지는 외부외란으로서 다루어질 수 있으며, 이러한 수중환경변화를 추정하기 위하여 외부외란에 영향을 받지 않는 PI 관측기를 제안한다. 이후 본 논문에서는 계단형 및 정현파형 수중환경변화를 가정하여 제안한 방법에 대해 시뮬레이션을 행하였으며, 그 결과 유효성을 확인하였다. 향후, 본 제안법을 ROV 시스템에 대한 햅틱제어기 설계에 이용하고자 한다.

• 대한기계학회논문집A
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• 제21권3호
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• pp.502-510
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• 1997

A disturbance rejection controller using eccentricity filtering and LQ control techniques is proposed to alleviate the effecto of major roll eccentricity in multivariable cold-rolling processes. Fundamental problems in multivariable cold-rolling processes such as process time delay inherent in exit thickness measurement and non-stationary characteristics of roll eccentricity signals can be overcome by the proposed control method. The filtered instantaneous estimate of roll eccentricity may be exploited to improve instantaneous estimate of the exit thickness variation based on roll force and roll gap measurements, and a feedforward compensator is augmented as a reference for a gaugemeter thickness estimator. LQ feedback controller is combined with eccentricity filter for the attenuation of the exit thickness variation due to the entry thickness variation. The simulation results show that the roll eccentricity disturbance is significantly eliminated and other disturbances also are attenuated.

• Transactions on Control, Automation and Systems Engineering
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• 제3권2호
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• pp.117-123
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• 2001

To improve control performance of a non-linear system, many other reserches have used the sliding model control algorithm. The sliding mode controller is known to be robust against nonlinear and unmodeled dynamic terms. However, this algorithm raises the inherent chattering caused by excessive switching inputs around the sliding surface. Therefore, in order to solve the chattering problem and improve control performance, this study has developed the sliding mode controller with a perturbation estimator using the observer-based fuzzy adaptive network. The perturbation estimator based on the fuzzy adaptive network generates the control input of compensating unmodeled dynamics terms and disturbance. And the weighting parameters of the fuzzy adaptive network are updated on-line by adaptive law in order to force the estimation errors converge to zero. Therefore, the combination of sliding mode control and fuzzy adaptive network gives rise to the robust and intelligent routine. For evaluation control performance of the proposed approach, tracking control simulation is carried is carried out for the hydraulic motion simulator which is a 6-degree of freedom parallel manipulator.